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Why On-Base Percentage is a Better Indicator of Future Performance than Batting Average: An Algebraic Proof

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  • Baumer Ben S

    (CUNY Graduate School and University Center)

Abstract

Batting Average (AVG) and On-Base Percentage (OBP) are two of the most commonly cited statistics in baseball. Existing research has demonstrated that for a team, OBP is more closely correlated to runs scored than is AVG, and secondly, for players, OBP is more closely correlated over time than is AVG. We offer an algebraic explanation for the latter phenomenon. Specifically, we will prove that batting average depends more heavily upon a particularly unpredictable variable, hits per balls in play (HPBP), than does OBP. This result will explain why for both batters and pitchers, on-base percentage is a better indicator of future performance than batting average.

Suggested Citation

  • Baumer Ben S, 2008. "Why On-Base Percentage is a Better Indicator of Future Performance than Batting Average: An Algebraic Proof," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(2), pages 1-13, April.
    • Handle: RePEc:bpj:jqsprt:v:4:y:2008:i:2:n:3
    DOI: 10.2202/1559-0410.1101
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    Cited by:

    1. Hamrick Jeff & Rasp John, 2011. "Using Local Correlation to Explain Success in Baseball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-29, October.
    2. McShane Blakeley B. & Braunstein Alexander & Piette James & Jensen Shane T., 2011. "A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-26, October.
    3. Weinstein-Gould Jesse, 2009. "Keeping the Hitter Off Balance: Mixed Strategies in Baseball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(2), pages 1-20, May.
    4. Null Brad, 2009. "Modeling Baseball Player Ability with a Nested Dirichlet Distribution," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(2), pages 1-38, May.
    5. Seong W. Kim & Sabina Shahin & Hon Keung Tony Ng & Jinheum Kim, 2021. "Binary segmentation procedures using the bivariate binomial distribution for detecting streakiness in sports data," Computational Statistics, Springer, vol. 36(3), pages 1821-1843, September.
    6. Vock David Michael & Vock Laura Frances Boehm, 2018. "Estimating the effect of plate discipline using a causal inference framework: an application of the G-computation algorithm," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 37-56, June.

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